package com.example.java.ai.langchain4j.config;

import com.example.java.ai.langchain4j.store.MongoChatMemoryStore;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.document.parser.TextDocumentParser;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

import java.nio.file.FileSystems;
import java.nio.file.PathMatcher;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;

@Configuration
public class XiaozhiAgentConfig {

    @Autowired
    MongoChatMemoryStore mongoChatMemoryStore;

    @Autowired
    EmbeddingStore embeddingStore;

    @Autowired
    EmbeddingModel embeddingModel;

    @Bean
    public ChatMemoryProvider chatMemoryProviderXiaozhi() {
        return memoryId -> MessageWindowChatMemory
                .builder()
                .id(memoryId)
                .maxMessages(1000)
                .chatMemoryStore(mongoChatMemoryStore)
                .build();
    }

    //@Bean
    public ContentRetriever contentRetrieverXiaozhi() {
        //读取指定目录下的知识库文档
        //并使用默认的文档解析器对文档进行解析
        //PathMatcher pathMatcher = FileSystems.getDefault().getPathMatcher("glob:*.txt");
        //List<Document> documents = FileSystemDocumentLoader.loadDocuments("/work/knowledge/", pathMatcher, new TextDocumentParser());
        Document document1 = FileSystemDocumentLoader.loadDocument("/work/knowledge/科室信息.md");
        Document document2 = FileSystemDocumentLoader.loadDocument("/work/knowledge/神经内科.md");
        List<Document> documents = Arrays.asList(document1, document2);
        //使用内存向量存储
        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
        //使用默认的文档分割器
        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
        //从嵌入存储(EmbeddingStore)里检索和查询内容相关的信息
        return EmbeddingStoreContentRetriever.from(embeddingStore);
    }

    @Bean
    public ContentRetriever contentRetrieverXiaozhiStore() {
        //创建一个　EmbeddingStoreContentRetriever　对象,用于从嵌入存储中检索内容
        return EmbeddingStoreContentRetriever.builder()
                //设置用于生成嵌入向量的嵌入模型
                .embeddingModel(embeddingModel)
                //指定要使用的嵌入存储
                .embeddingStore(embeddingStore)
                //设置最大检索结果数量,这里表示最多返回１条匹配结果
                .maxResults(1)
                //设置最小得分阈值,只有得分大于等于０.８的结果才会被返回
                .minScore(0.8)
                //构建最终的　EmbeddingStoreContentRetriever　实例
                .build();
    }


}
